Terrificfantasm/bert-base-uncased-ud-ewt-pos
token-classification model by Terrificfantasm
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Model card & documentation
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Selected method: partialfinetuning, epoch 3. The choice was fixed on validation data before test evaluation. This is a measured academic adaptation of BERT, not a production-validated system. UD English EWT r2.15, official 12,544 / 2,001 / 2,077 sentence splits. Integer-ID syntactic words only; skip multiword surface rows and empty nodes. Exact overlaps retained: 36 / 47 / 27 across train/val, train/test, val/test. Validation word accuracy; test word accuracy and macro-F1 over all 17 UPOS tags. BERT-base-uncased with a linear 17-class head. First-subtoken supervision; other positions use -100. No truncation; dynamic padding. The table preserves native scales: AG News, NER and POS metrics are in [0, 1]; QA EM/F1 are in [0, 100]. Training seconds exclude validation, saving and final evaluation. Three epochs, seed 42. AdamW: head LR 1e-3, trainable encoder LR 2e-5, weight decay 0.01, 10% linear warmup, clipping 1.0. Effective batch size 16. A partial method trains only the…
Read the full model card ↗ · Preview checked 2026-11-10T16:55:41.170Z
Inside the original model card — Document outline
- UD English EWT part-of-speech tagging
- Training data and evaluation
- Optimization and reproducibility
- Intended use and limitations
- Loading the delivered model
- Source terms and references
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Model overview
Terrificfantasm/bert-base-uncased-ud-ewt-pos is a token-classification model repository published by Terrificfantasm on Hugging Face. The source reports the transformers library.
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Model facts
- Task
- token-classification
- Library
- transformers
- Recent downloads (30 days)
- 0
- Cumulative likes
- 0
- Hugging Face trending score
- 0
- Reported safetensors parameters
- 108,904,721
- Architecture
- BertForTokenClassification
- License
- Not reported — check the model card
- Access
- Not gated by Hugging Face
- Created
- 2026-09-27T06:27:42.000Z
- Last modified
- 2026-09-27T06:31:29.000Z
Compatibility and lineage
Reported languages: en
Reported base models: google-bert/bert-base-uncased
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-09-27T06:37:53.622Z. Daily imports are snapshots, not real-time monitoring.
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